Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

The rumen ecosystem harbours a galaxy of microbes working in syntrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation-based approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, the integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this paper, we aim to discuss the potential use of two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis approaches. We will discuss how these methods can be used to produce the next-generation models of the rumen microbiome.

P–688 Assessment of ovarian vascularity by three-dimensional vaginal power Doppler on day two of menstrual cycle to predict the number of mature eggs collected

Study question

Could ovarian vascularity indices, measured by 3-dimensional (3D) vaginal power Doppler, predict the number of mature oocytes collected after controlled ovarian stimulation?

Summary answer

Ovarian vascularity index (VI) may be an indicator of poor (<three mature eggs collected) and high (>ten mature eggs collected) ovarian responses to gonadotropins.

What is known already

Poor and/or hyper ovarian responses to gonadotropins may be related to cycle cancellation during controlled ovarian stimulation (COS). In this context, gonadotropin dose is often individualized using patient features that predict ovarian response (such as age, antral follicular count (AFC) and anti-Müllerian hormone (AMH)). In parallel, ovarian vascularity color doppler is a valuable evaluation method to predict the ovarian hyperstimulation syndrome and the growth/maturity of Graafian follicles. The aim of the present study is to estimate the utility of 3-dimensional vaginal power Doppler and ovarian vascular flow indices in the prediction of the number of mature occytes collected after COS.

Study design, size, duration

A prospective study was conducted on 200 couples undergoing intracytoplasmic sperm injection cycle at Al Hadi Laboratory and Medical center, Beirut, Lebanon. It was performed between January 2020 and July 2020. Couples were categorized into poor responders group (3 or less metaphase II (MII) eggs collected) (n = 43), high responders group (10 or more MII eggs collected) group (n = 66), and normal responders group (more than 3 and less than 10 MII eggs collected) (n = 66).

Participants/materials, setting, methods

On the second day of the menstrual cycle, ovarian volume and vascularity parameters (vascularity index (VI), flow index (FI), and vascularity flow index (VFI)) were measured using the 3D power Doppler and the Virtual Organ Computer-Aided Analysis. On the same day, the antral follicle count was evaluated and a blood sample for AMH testing was collected. Women included in the study have undergone COS using GnRH antagonist protocol.

Main results and the role of chance

Receiver operator characteristics (ROC) curve model was used to predict the number of mature eggs collected. 7 parameters were used to predict poor and high ovarian responses (Age, AMH, AFC, ovarian volume, VI, FI and VFI). Ovarian VI significantly predicted poor ovarian response to gonadotropins (p = 0.033 and area under the curve (AUC)=0.668). Subsequently, the cut off value was 0.0025 with 84% sensitivity and 83.3% specificity. In parallel, ovarian VI significantly predicted high ovarian response to gonadotropins (p = 0.036 and AUC (0.778)) with a cut off value 0.0375 and with 77.8% sensitivity and 78.3% specificity. Furthermore, VFI significantly predicted high ovarian response to gonadotropins (p = 0.045; AUC=0.677).

Limitations, reasons for caution

It will be necessary to perform a prospective analysis on a broad sample size to validate these findings. In addition, it will be interesting to assess the impact of ovarian vascularity on pregnancy outcomes.

Wider implications of the findings: Assessing ovarian vascularity prior to ovarian stimulation can help reduce the rate of cycle cancellation. In addition, more studies are welcomed in the field to unravel the mechanisms behind altered ovarian vascularity and to test the possibility of restoring normal ovarian physiology.

Trial registration number

Not applicable

Identification of Shiga toxin-producing (STEC) and enteropathogenic (EPEC) Escherichia coli in diarrhoeic calves and comparative genomics of O5 bovine and human STEC

Escherichia coli producing Shiga toxins (Stx) and the attaching-effacing (AE) lesion (AE-STEC) are responsible for (bloody) diarrhoea in humans and calves while the enteropathogenic E. coli (EPEC) producing the AE lesion only cause non-bloody diarrhoea in all mammals. The purpose of this study was (i) to identify the pathotypes of enterohaemolysin-producing E. coli isolated between 2009 and 2013 on EHLY agar from less than 2 month-old diarrhoeic calves with a triplex PCR targeting the stx1, stx2, eae virulence genes; (ii) to serotype the positive isolates with PCR targeting the genes coding for ten most frequent and pathogenic human and calf STEC O serogroups; and (iii) to compare the MLSTypes and virulotypes of calf and human O5 AE-STEC after Whole Genome Sequencing using two server databases (www.genomicepidemiology.org). Of 233 isolates, 206 were triplex PCR-positive: 119 AE-STEC (58%), 78 EPEC (38%) and 9 STEC (4%); and the stx1+eae+ AE-STEC (49.5%) were the most frequent. Of them, 120 isolates (84% of AE-STEC, 23% of EPEC, 22% of STEC) tested positive with one O serogroup PCR: 57 for O26 (47.5%), 36 for O111 (30%), 10 for O103 (8%) and 8 for O5 (7%) serogroups. The analysis of the draft sequences of 15 O5 AE-STEC could not identify any difference correlated to the host. As a conclusion, (i) the AE-STEC associated with diarrhoea in young calves still belong to the same serogroups as previously (O5, O26, O111) but the O103 serogroup may be emerging, (ii) the O5 AE-STEC from calves and humans are genetically similar.